| run.it.importances {RFlocalfdr} | R Documentation |
run.it.importances
Description
run.it.importances
Usage
run.it.importances(qq, imp, debug.flag = 0, temp.dir = NULL)
Arguments
qq |
object retuned by plotQ |
imp |
"reduction in impurity" importances from a random forest model |
debug.flag |
either 0 (no debugging information), 1 or 2 |
temp.dir |
if debug flag is >0 then information is written to temp.dir |
Value
return a list contining
"C_0.95" estimate of the cutoff "C" such that there are only null values to the left of C. Based on the 95th quantile of the density
"cc" estimate of the cutoff "C" based on the procedure of Gauran, et al., 2918, Biometrics,
"estimates_C_0.95" estimate of the parameters of the SN using the data up to the C estimate
"estimates_cc" estimate of the parameters of the SN using the data up to the C estimate
"fdr_0.95" estimate of the fdr curve using the SN from "estimates_C_0.95"
"fdr_cc" estimate of the fdr curve using the SN from "estimates_cc"
"x" the x values from plotQ
"temp.dir" the temp directory for dubuggin
"p0" the estmate of the proportion of null values (can be 1)
Examples
data(imp20000)
imp <- log(imp20000$importances)
t2 <- imp20000$counts
plot(density((imp)))
hist(imp,col=6,lwd=2,breaks=100,main="histogram of importances")
res.temp <- determine_cutoff(imp, t2 ,cutoff=c(0,1,2,3),plot=c(0,1,2,3),Q=0.75,try.counter=1)
plot(c(0,1,2,3),res.temp[,3])
imp<-imp[t2 > 1]
qq <- plotQ(imp,debug.flag = 0)
ppp<-run.it.importances(qq,imp,debug=0)
aa<-significant.genes(ppp,imp,cutoff=0.2,debug.flag=0,do.plot=2, use_95_q=TRUE)
length(aa$probabilities) #11#
names(aa$probabilities)
library(RFlocalfdr.data)
data(ch22)
? ch22
#document how the data set is created
plot(density(log(ch22$imp)))
t2 <-ch22$C
imp<-log(ch22$imp)
#Detemine a cutoff to get a unimodal density.
res.temp <- determine_cutoff(imp, t2 ,cutoff=c(1,10,20,30),plot=c(1,10,20,30),Q=0.75)
plot(c(1,2,3,4),res.temp[,3])
res.temp <- determine_cutoff(imp, t2 ,cutoff=c(25,30,35,40),plot=c(25,30,35,40),Q=0.75)
plot(c(25,30,35,40),res.temp[,3])
imp<-imp[t2 > 30]
qq <- plotQ(imp,debug.flag = 0)
ppp<-run.it.importances(qq,imp,debug=0)
aa<-significant.genes(ppp,imp,cutoff=0.2,debug.flag=0,do.plot=2)
length(aa$probabilities) # 6650
aa<-significant.genes(ppp,imp,cutoff=0.05,debug.flag=0,do.plot=2)
length(aa$probabilities) # 3653